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To Quit or Not to Quit: Predicting Future Behavioral Disengagement from Reading Patterns

  • Caitlin Mills
  • Nigel Bosch
  • Art Graesser
  • Sidney D’Mello
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8474)

Abstract

This research predicted behavioral disengagement using quitting behaviors while learning from instructional texts. Supervised machine learning algorithms were used to predict if students would quit an upcoming text by analyzing reading behaviors observed in previous texts. Behavioral disengagement (quitting) at any point during the text was predicted with an accuracy of 76.5% (48% above chance), before students even began engaging with the text. We also predicted if a student would quit reading on the first page of a text or continue reading past the first page with an accuracy of 88.5% (29% above chance), as well as if students would quit sometime after the first page with an accuracy of 81.4% (51% greater than chance). Both actual quits and predicted quits were significantly related to learning, which provides some evidence for the predictive validity of our model. Implications and future work related to ITSs are also discussed.

Keywords

engagement disengagement affect detection reading ITSs 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Caitlin Mills
    • 1
  • Nigel Bosch
    • 1
  • Art Graesser
    • 2
  • Sidney D’Mello
    • 1
  1. 1.Departments of Psychology and Computer ScienceUniversity of Notre DameNotre DameUSA
  2. 2.Department of Psychology and Institute for Intelligent SystemsUniversity of MemphisMemphisUSA

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